| Summary: | Landslides in the landscape exhibit predictable properties of shape, structure and orientation. These properties are reflected to varying degrees in their depiction
in a satellite image. Landslides can be isolated along with similar objects in a digital image using differential and template operators. Extraction of the landslide features from these images can proceed using a logic-based model which draws on an appropriate object definition approximating the depiction of the landslides in an edge-operated image and a digital elevation model.
An object extraction algorithm based on these concepts is used in repeated trials to ascertain the effectiveness of this automated approach. A low resolution linear object definition (Fischler et al. , 1981) is used to isolate candidate
pixel segments in three enhanced images. These segments are classified as landslides or non-landslides according to their image pixel intensity, length, slope, and orientation. Digital elevation data is used to evaluate slope and orientation criteria. Results are compared to an inventory of landslides made using aerial photographs.
Study results indicate that 17% to 28% of landslides in the image are identified for trials that produce a commission error rate of less than 50%. Commission errors are dominated by image objects related to roads and waste wood areas in clearcuts. A higher rate of successful identification was noted for landslides which occurred within 15 years of image acquisition (24% to 32%), and was most apparent for the subset of that group which was located in areas that were harvested more than 15 years before acquisition or were unharvested (29% to 38%). Successful identifications in the trials are dominated by events greater than 300 metres long and wider than 20 metres. The results suggest
that the approach is more reliable in unharvested areas of the image.
The poor quality of the digital elevation data, specifically artifacts produced by the contour-to-grid algorithm, was partly responsible for errors of commission and omission. The simplicity of the object definition used is another factor in error production. The methodology is not operational, but represents a realistic approach to scene segmentation for resource management given further refinement. === Forestry, Faculty of === Graduate
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